OpenRaiser/Intern-Atlas huggingface dataset:
Decomposition of bent generalized Boolean functions
Venue: arXiv cs.IT 2016
Year: 2016

A one to one correspondence between regular generalized bent functions from $\F_2^n$ to $\Z_{2^m},$ and $m-$tuples of Boolean bent functions is established. This correspondence maps self-dual (resp. anti-self-dual) generalized bent functions to $m-$tuples of self-dual (resp. anti self-dual) Boolean bent functions. An application to the classification of regular generalized bent functions under the extended affine group is given.
A Case for Malleable Thread-Level Linear Algebra Libraries: The LU Factorization with Partial Pivoting
Venue: arXiv cs.DC 2016
Year: 2016

We propose two novel techniques for overcoming load-imbalance encountered when implementing so-called look-ahead mechanisms in relevant dense matrix factorizations for the solution of linear systems. Both techniques target the scenario where two thread teams are created/activated during the factorization, with each team in charge of performing an independent task/branch of execution. The first technique promotes worker sharing (WS) between the two tasks, allowing the threads of the task that completes first to be reallocated for use by the costlier task. The second technique allows a fast task to alert the slower task of completion, enforcing the early termination (ET) of the second task, and a smooth transition of the factorization procedure into the next iteration. The two mechanisms are instantiated via a new malleable thread-level implementation of the Basic Linear Algebra Subprograms (BLAS), and their benefits are illustrated via an implementation of the LU factorization with partial pivoting enhanced with look-ahead. Concretely, our experimental results on a six core Intel-Xeon processor show the benefits of combining WS+ET, reporting competitive performance in comparison with a task-parallel runtime-based solution.
Active and Transfer Learning of Grasps by Kernel Adaptive MCMC
Venue: arXiv cs.RO 2016
Year: 2016

Human ability of both versatile grasping of given objects and grasping of novel (as of yet unseen) objects is truly remarkable. This probably arises from the experience infants gather by actively playing around with diverse objects. Moreover, knowledge acquired during this process is reused during learning of how to grasp novel objects. We conjecture that this combined process of active and transfer learning boils down to a random search around an object, suitably biased by prior experience, to identify promising grasps. In this paper we present an active learning method for learning of grasps for given objects, and a transfer learning method for learning of grasps for novel objects. Our learning methods apply a kernel adaptive Metropolis-Hastings sampler that learns an approximation of the grasps' probability density of an object while drawing grasp proposals from it. The sampler employs simulated annealing to search for globally-optimal grasps. Our empirical results show promising applicability of our proposed learning schemes.
On Embeddings of $\ell_1^k$ from Locally Decodable Codes
Venue: arXiv cs.CC 2016
Year: 2016

We show that any $q$-query locally decodable code (LDC) gives a copy of $\ell_1^k$ with small distortion in the Banach space of $q$-linear forms on $\ell_{p_1}^N\times\cdots\times\ell_{p_q}^N$, provided $1/p_1 + \cdots + 1/p_q \leq 1$ and where $k$, $N$, and the distortion are simple functions of the code parameters. We exhibit the copy of $\ell_1^k$ by constructing a basis for it directly from "smooth" LDC decoders. Based on this, we give alternative proofs for known lower bounds on the length of 2-query LDCs. Using similar techniques, we reprove known lower bounds for larger $q$. We also discuss the relation with an alternative proof, due to Pisier, of a result of Naor, Regev, and the author on cotype properties of projective tensor products of $\ell_p$ spaces.
Formalizing Multi-Agent Systems Using Action Descriptions in Single Agent Perspective
Venue: arXiv cs.MA 2016
Year: 2016

Logic-based representations of multi-agent systems have been extensively studied. In this work, we focus on the action language BC to formalize global views of MAS domains. Methodologically, we start representing the behaviour of each agent by an action description from a single agent perspective. Then, it goes through two stages that guide the modeler in composing the global view by first designating multi-agent aspects of the domain via potential conflicts and later resolving these conflicts according to the expected behaviour of the overall system. Considering that representing single agent descriptions is relatively simpler than representing multi-agent description directly, the formalization developed here is valuable from a knowledge representation perspective.
Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline
Venue: arXiv cs.LG 2016
Year: 2016

We propose a simple but strong baseline for time series classification from scratch with deep neural networks. Our proposed baseline models are pure end-to-end without any heavy preprocessing on the raw data or feature crafting. The proposed Fully Convolutional Network (FCN) achieves premium performance to other state-of-the-art approaches and our exploration of the very deep neural networks with the ResNet structure is also competitive. The global average pooling in our convolutional model enables the exploitation of the Class Activation Map (CAM) to find out the contributing region in the raw data for the specific labels. Our models provides a simple choice for the real world application and a good starting point for the future research. An overall analysis is provided to discuss the generalization capability of our models, learned features, network structures and the classification semantics.
Decision-Based Transcription of Jazz Guitar Solos Using a Harmonic Bident Analysis Filter Bank and Spectral Distribution Weighting
Venue: arXiv cs.SD 2016
Year: 2016

Jazz guitar solos are improvised melody lines played on one instrument on top of a chordal accompaniment (comping). As the improvisation happens spontaneously, a reference score is non-existent, only a lead sheet. There are situations, however, when one would like to have the original melody lines in the form of notated music, see the Real Book. The motivation is either for the purpose of practice and imitation or for musical analysis. In this work, an automatic transcriber for jazz guitar solos is developed. It resorts to a very intuitive representation of tonal music signals: the pitchgram. No instrument-specific modeling is involved, so the transcriber should be applicable to other pitched instruments as well. Neither is there the need to learn any note profiles prior to or during the transcription. Essentially, the proposed transcriber is a decision tree, thus a classifier, with a depth of 3. It has a (very) low computational complexity and can be run on-line. The decision rules can be refined or extended with no or little musical education. The transcriber's performance is evaluated on a set of ten jazz solo excerpts and compared with a state-of-the-art transcription system for the guitar plus PYIN. We achieve an improvement of 34% w.r.t. the reference system and 19% w.r.t. PYIN in terms of the F-measure. Another measure of accuracy, the error score, attests that the number of erroneous pitch detections is reduced by more than 50% w.r.t. the reference system and by 45% w.r.t. PYIN.
Coded Caching with Distributed Storage
Venue: arXiv cs.IT 2016
Year: 2016

Content delivery networks store information distributed across multiple servers, so as to balance the load and avoid unrecoverable losses in case of node or disk failures. Coded caching has been shown to be a useful technique which can reduce peak traffic rates by pre-fetching popular content at the end users and encoding transmissions so that different users can extract different information from the same packet. On one hand, distributed storage limits the capability of combining content from different servers into a single message, causing performance losses in coded caching schemes. But, on the other hand, the inherent redundancy existing in distributed storage systems can be used to improve the performance of those schemes through parallelism. This paper designs a scheme combining distributed storage of the content in multiple servers and an efficient coded caching algorithm for delivery to the users. This scheme is shown to reduce the peak transmission rate below that of state-of-the-art algorithms.
Control of nonlinear switched systems based on validated simulation
Venue: arXiv cs.SY 2016
Year: 2016

We present an algorithm of control synthesis for nonlinear switched systems, based on an existing procedure of state-space bisection and made available for nonlinear systems with the help of validated simulation. The use of validated simulation also permits to take bounded perturbations and varying parameters into account. It is particularly interesting for safety critical applications, such as in aeronautical, military or medical fields. The whole approach is entirely guaranteed and the induced controllers are correct-by-design.
OSSINT - Open Source Social Network Intelligence An efficient and effective way to uncover "private" information in OSN profiles
Venue: arXiv cs.SI 2016
Year: 2016

Online Social Networks (OSNs), such as Facebook, provide users with tools to share information along with a set of privacy controls preferences to regulate the spread of information. Current privacy controls are efficient to protect content data. However, the complexity of tuning them undermine their efficiency when protecting contextual information (such as the social network structure) that many users believe being kept private. In this paper, we demonstrate the extent of the problem of information leakage in Facebook. In particular, we show the possibility of inferring, from the network "surrounding" a victim user, some information that the victim set as hidden. We developed a system, named OSSINT (Open Source Social Network INTelligence), on top of our previous tool SocialSpy, that is able to infer hidden information of a victim profile and retrieve private information from public one. OSSINT retrieves the friendship network of a victim and shows how it is possible to infer additional private information (e.g., user personal preferences and hobbies). Our proposed system OSSINT goes extra mile about the network topology information, i.e., predicting new friendships using the victim's friends of friends network (2-hop of distance from the victim profile), and hence possibly deduce private information of the full Facebook network. OSSINT correctly improved the previous results of SocialSpy predicting an average of 11 additional friendships with peaks of 20 new friends. Moreover, OSSINT, for the considered victim profiles demonstrated how it is possible to infer real life information such as current city, hometown, university, supposed being private.
Demonstration of a context-switch method for heterogeneous reconfigurable systems
Venue: arXiv cs.DC 2016
Year: 2016

Nowadays, FPGAs are integrated in high-performance computing systems, servers, or even used as accelerators in System-on-Chip (SoC) platforms. Since the execution is performed in hardware, FPGA gives much higher performance and lower energy consumption compared to most microprocessor-based systems. However, the room to improve FPGA performance still exists, e.g. when it is used by multiple users. In multi-user approaches, FPGA resources are shared between several users. Therefore, one must be able to interrupt a running circuit at any given time and continue the task at will. An image of the state of the running circuit (context) is saved during interruption and restored when the execution is continued. The ability to extract and restore the context is known as context-switch.In the previous work [1], an automatic checkpoint selection method is proposed for circuit generation targeting reconfigurable systems. The method relies on static analysis of the finite state machine of a circuit to select the checkpoint states. States with minimum overhead will be selected as checkpoints, which allow optimal context save and restore. The maximum time to reach a checkpoint will be defined by the user and consideredas the context-switch latency. The method is implemented in C code and integrated as plugin in a free and open-source High-Level Synthesis tool AUGH [2].
Security Schemes in Vehicular Ad hoc Networks with Cognitive Radios
Venue: arXiv cs.NI 2016
Year: 2016

Vehicular Ad hoc NETworks (VANETs) as the basic infrastructure can facilitate applications and services of connected vehicles (CVs). Cognitive radio (CR) technology is an effective supplement and enhancement for VANETs. It can reduce the impact of deficiency of spectrum resource in VANETs. Although CR-VANETs can utilize the unused licensed spectrum effectively, the distributed nature of CR-VANETs may open a door for different attacks, such as spectrum sensing data falsification attack. In this paper, we propose a joint RSU and vehicle-based light-weighted cloud for CR-VANETs. Based on this cloud computing model, we propose a new service named Spectrum Sensing as a Service (SSaaS), which can perform a cooperative spectrum sensing in CR-VANETs with cloud computing assistance to secure the spectrum sensing procedure. As a result, a reliable service can be obtained in CR-VANETs. Simulation results show that the cloud computing in CR-VANETs can effectively reduce latency and improve the security of CR-VANETs.
Robust Design of H-infinity Controller for a Launch Vehicle Autopilot against Disturbances
Venue: arXiv cs.SY 2016
Year: 2016

Atmospheric flight phase of a launch vehicle is utilized to evaluate the performance of an H-infinity controller in the presence of disturbances. Dynamics of the vehicle is linearly modeled using time-varying parameters. An operating point was found to design a robust command tracker using H-infinity control theory that guarantees a stable maneuver. At the end, the controller was employed on the launch vehicle to assess the capability of control design on the linearized aerospace vehicle. Experimental results illustrate the excellent performance of the H-infinity controller and accurate tracking implemented by the autopilot. Also the robustness of the entire system against disturbances is demonstrated to be acceptable.
Using inspiration from synaptic plasticity rules to optimize traffic flow in distributed engineered networks
Venue: arXiv cs.NE 2016
Year: 2016

Controlling the flow and routing of data is a fundamental problem in many distributed networks, including transportation systems, integrated circuits, and the Internet. In the brain, synaptic plasticity rules have been discovered that regulate network activity in response to environmental inputs, which enable circuits to be stable yet flexible. Here, we develop a new neuro-inspired model for network flow control that only depends on modifying edge weights in an activity-dependent manner. We show how two fundamental plasticity rules (long-term potentiation and long-term depression) can be cast as a distributed gradient descent algorithm for regulating traffic flow in engineered networks. We then characterize, both via simulation and analytically, how different forms of edge-weight update rules affect network routing efficiency and robustness. We find a close correspondence between certain classes of synaptic weight update rules derived experimentally in the brain and rules commonly used in engineering, suggesting common principles to both.
Predicting 1p19q Chromosomal Deletion of Low-Grade Gliomas from MR Images using Deep Learning
Venue: arXiv cs.CV 2016
Year: 2016

Objective: Several studies have associated codeletion of chromosome arms 1p/19q in low-grade gliomas (LGG) with positive response to treatment and longer progression free survival. Therefore, predicting 1p/19q status is crucial for effective treatment planning of LGG. In this study, we predict the 1p/19q status from MR images using convolutional neural networks (CNN), which could be a noninvasive alternative to surgical biopsy and histopathological analysis. Method: Our method consists of three main steps: image registration, tumor segmentation, and classification of 1p/19q status using CNN. We included a total of 159 LGG with 3 image slices each who had biopsy-proven 1p/19q status (57 nondeleted and 102 codeleted) and preoperative postcontrast-T1 (T1C) and T2 images. We divided our data into training, validation, and test sets. The training data was balanced for equal class probability and then augmented with iterations of random translational shift, rotation, and horizontal and vertical flips to increase the size of the training set. We shuffled and augmented the training data to counter overfitting in each epoch. Finally, we evaluated several configurations of a multi-scale CNN architecture until training and validation accuracies became consistent. Results: The results of the best performing configuration on the unseen test set were 93.3% (sensitivity), 82.22% (specificity), and 87.7% (accuracy). Conclusion: Multi-scale CNN with their self-learning capability provides promising results for predicting 1p/19q status noninvasively based on T1C and T2 images. Significance: Predicting 1p/19q status noninvasively from MR images would allow selecting effective treatment strategies for LGG patients without the need for surgical biopsy.
Full and Fractional Counting in Bibliometric Networks
Venue: arXiv cs.DL 2016
Year: 2016

In their study entitled "Constructing bibliometric networks: A comparison between full and fractional counting," Perianes-Rodriguez, Waltman, & van Eck (2016; henceforth abbreviated as PWvE) provide arguments for the use of fractional counting at the network level as different from the level of publications. Whereas fractional counting in the latter case divides the credit among co-authors (countries, institutions, etc.), fractional counting at the network level can normalize the relative weights of links and thereby clarify the structures in the network. PWvE, however, propose a counting scheme for fractional counting that is one among other possible ones. Alternative schemes proposed by Batagelj and Cerinšek (2013) and Park, Yoon, & Leydesdorff (2016; henceforth abbreviated as PYL) are discussed in an appendix. However, our approach is not correctly identified as identical to their Equation A3. Here below, we distinguish three approaches analytically; routines for applying these approaches to bibliometric data are also provided.
Catch'Em All: Locating Multiple Diffusion Sources in Networks with Partial Observations
Venue: arXiv cs.SI 2016
Year: 2016

This paper studies the problem of locating multiple diffusion sources in networks with partial observations. We propose a new source localization algorithm, named Optimal-Jordan-Cover (OJC). The algorithm first extracts a subgraph using a candidate selection algorithm that selects source candidates based on the number of observed infected nodes in their neighborhoods. Then, in the extracted subgraph, OJC finds a set of nodes that "cover" all observed infected nodes with the minimum radius. The set of nodes is called the Jordan cover, and is regarded as the set of diffusion sources. Considering the heterogeneous susceptible-infected-recovered (SIR) diffusion in the Erdos-Renyi (ER) random graph, we prove that OJC can locate all sources with probability one asymptotically with partial observations. OJC is a polynomial-time algorithm in terms of network size. However, the computational complexity increases exponentially in $m,$ the number of sources. We further propose a low-complexity heuristic based on the K-Means for approximating the Jordan cover, named Approximate-Jordan-Cover (AJC). Simulations on random graphs and real networks demonstrate that both AJC and OJC significantly outperform other heuristic algorithms.
An Efficient Training Algorithm for Kernel Survival Support Vector Machines
Venue: arXiv cs.LG 2016
Year: 2016

Survival analysis is a fundamental tool in medical research to identify predictors of adverse events and develop systems for clinical decision support. In order to leverage large amounts of patient data, efficient optimisation routines are paramount. We propose an efficient training algorithm for the kernel survival support vector machine (SSVM). We directly optimise the primal objective function and employ truncated Newton optimisation and order statistic trees to significantly lower computational costs compared to previous training algorithms, which require $O(n^4)$ space and $O(p n^6)$ time for datasets with $n$ samples and $p$ features. Our results demonstrate that our proposed optimisation scheme allows analysing data of a much larger scale with no loss in prediction performance. Experiments on synthetic and 5 real-world datasets show that our technique outperforms existing kernel SSVM formulations if the amount of right censoring is high ($\geq85\%$), and performs comparably otherwise.
Leveraging Citation Networks to Visualize Scholarly Influence Over Time
Venue: arXiv cs.HC 2016
Year: 2016

Assessing the influence of a scholar's work is an important task for funding organizations, academic departments, and researchers. Common methods, such as measures of citation counts, can ignore much of the nuance and multidimensionality of scholarly influence. We present an approach for generating dynamic visualizations of scholars' careers. This approach uses an animated node-link diagram showing the citation network accumulated around the researcher over the course of the career in concert with key indicators, highlighting influence both within and across fields. We developed our design in collaboration with one funding organization---the Pew Biomedical Scholars program---but the methods are generalizable to visualizations of scholarly influence. We applied the design method to the Microsoft Academic Graph, which includes more than 120 million publications. We validate our abstractions throughout the process through collaboration with the Pew Biomedical Scholars program officers and summative evaluations with their scholars.
A Natural Language Query Interface for Searching Personal Information on Smartwatches
Venue: arXiv cs.HC 2016
Year: 2016

Currently, personal assistant systems, run on smartphones and use natural language interfaces. However, these systems rely mostly on the web for finding information. Mobile and wearable devices can collect an enormous amount of contextual personal data such as sleep and physical activities. These information objects and their applications are known as quantified-self, mobile health or personal informatics, and they can be used to provide a deeper insight into our behavior. To our knowledge, existing personal assistant systems do not support all types of quantified-self queries. In response to this, we have undertaken a user study to analyze a set of "textual questions/queries" that users have used to search their quantified-self or mobile health data. Through analyzing these questions, we have constructed a light-weight natural language based query interface, including a text parser algorithm and a user interface, to process the users' queries that have been used for searching quantified-self information. This query interface has been designed to operate on small devices, i.e. smartwatches, as well as augmenting the personal assistant systems by allowing them to process end users' natural language queries about their quantified-self data.